Entry requirements

120 credits and Automatic control

Learning outcomes

Students who pass the course should be able to

• determine relations between multivariable dynamic models in form of state space models, transfer functions, and poles and zeros
• analyse multivariable dynamic systems with respect to stability, robustness, sensitivity for disturbances, steady state properties, and controllability and observability
• analyse non-linear dynamic systems in the phase plane, and to perform stability analysis with the aid of Lyapunov techniques
• analyse dynamic systems influenced by noise, and to determine stationary variances for given linear models
• design optimal observers (Kalman filters)
• analyse given specifications for a control system and decide if these can be fulfiled, and if the control problem is hard or not
• design controllers for linear multivariable systems based on internal model control, linear quadratic (LQ) control, optimisation of H-2 and H-infinity criteria, and robust control
• evaluate controllers in laboratory work on real processes

Content

Observers and state feedback control. Mathematical description of linear multivariable systems. Controllability and observability. Stability. Description of disturbances. Controller synthesis. Sensitivity and robustness for multivariable systems. Theoretical limitations of performance. Linear quadratic theory and the separation theorem. The LTR approach. Robust loop shaping. Prediction control. Analysis of systems with simple non linearities.

Instruction

Lectures, problem solving sessions, tutorials and laboratory work. Non compulsory homework assignments.

Assessment

Written examination at the end of the course. Passed laboratory course is also required, as well as attendance at guest lectures (if any).